In 2024, a global manufacturing company ran 23 AI pilots across its business units. The pilots worked. Chatbots answered questions. Document processors extracted data. Forecasting models outperformed spreadsheets. Leadership declared success and… nothing changed. A year later, exactly zero of those pilots had reached production. The company had proven AI could work; they hadn’t proven it could deliver value at scale.
This story repeats across enterprises worldwide. According to research from MIT, 95% of AI pilots fail to deliver measurable business value—most never scale beyond the experimental phase. In 2025, the average enterprise scrapped 46% of AI pilots before they ever reached production. Global investment in generative AI solutions more than tripled to roughly $37 billion in 2025, yet 74% of companies still struggle to scale their AI initiatives into real business impact.
Why do some organizations break through while others remain trapped in what we call “pilot purgatory”? The answer isn’t technology—it’s how organizations approach the transition from experiment to production.
The Pilot Trap
Most enterprises approach AI the same way. They identify an interesting use case, assemble a team, run a pilot, declare success, and then stall. The pilot proved the technology works, but scaling requires investment, change management, and governance that organizations aren’t prepared to provide. The result is a graveyard of successful experiments that never delivered business value.
The symptoms are unmistakable. Organizations have multiple proof-of-concepts but zero production deployments. Data science teams are enthusiastic while business stakeholders remain skeptical. There’s a “we did AI” checkbox without measurable outcomes to show for it. Security and compliance concerns block production deployment. No one owns the responsibility for scaling successful pilots into real operations.
The ISG State of Enterprise AI Adoption Report 2025 quantifies this problem: only about one in four AI initiatives actually deliver their expected ROI, and fewer than 20% have been fully scaled across the enterprise. In a survey of 120,000+ enterprise respondents, only 8.6% of companies report having AI agents deployed in production, while 63.7% report no formalized AI initiative at all. The gap between AI adoption and AI value remains stubbornly wide.
What Successful Organizations Do Differently
1. Start with Business Problems, Not Technology
Failed AI initiatives typically start with “We should use AI for something.” Successful ones start with “This business problem costs us $X million annually—can AI help?” The difference matters enormously.
Business problems come with budgets and executive sponsors who have a stake in the outcome. Clear problems have measurable success criteria that everyone can agree on. Stakeholders are invested in solutions rather than experiments. When a pilot solves a quantified problem, the case for scaling writes itself.
Before launching any AI initiative, quantify the business problem. If you can’t put a dollar figure on it, you probably don’t have the executive sponsorship needed to scale. The successful implementations follow what researchers call a counterintuitive split: 10% on algorithms, 20% on infrastructure, 70% on people and process. That last 70% requires business ownership, not just technical enthusiasm.
2. Build Governance from Day One
Pilots often skip governance because “we’ll figure it out later.” But when “later” arrives, the lack of logging, security controls, and compliance documentation blocks production deployment. Security teams rightfully refuse to approve systems they can’t audit. Compliance finds gaps that require redesign. What should have been a straightforward scale becomes a rebuild.
Organizations that scale AI treat governance as a feature, not an afterthought. Security and compliance stakeholders are involved from the start. Logging and monitoring are built into the MVP, not bolted on later. Data handling practices are documented before production. Risk assessments happen during design, not after deployment.
For a comprehensive framework on what governance should include, our CISO AI Governance Checklist provides the full requirements. The key insight: governance built early accelerates production; governance added late delays or blocks it entirely.
3. Measure Outcomes, Not Activity
“The chatbot handled 10,000 conversations” sounds impressive—but did it reduce support costs? Improve customer satisfaction? Drive revenue? Activity metrics are easy to collect but often misleading. Outcome metrics are harder to define but actually prove value.
Activity metrics track what the AI does: chatbot conversations, AI completions, agent tasks, documents processed. Outcome metrics track what the business gains: cost savings, time saved, revenue impact, error reduction, customer satisfaction changes. The difference between “we processed 50,000 invoices” and “we reduced invoice processing costs by 60%” is the difference between a pilot that stalls and one that scales.
Define outcome metrics before the pilot begins. Establish baselines so you can prove improvement. Our AI ROI measurement framework provides a structured approach to connecting AI activity to business outcomes.
4. Plan for Change Management
AI that changes workflows requires people to change behavior. Without change management, even great technology fails. Employees resist tools they don’t understand. Workarounds emerge that bypass the AI entirely. Training gaps lead to misuse and disappointment. The technology works but the adoption doesn’t.
Successful organizations plan for adoption from the beginning. They involve end users in design and testing, building tools that fit how people actually work. They create training and documentation before launch, not after complaints pile up. They measure adoption rates and address resistance directly rather than hoping it resolves itself. They iterate based on user feedback, treating the human side of deployment as seriously as the technical side.
Include change management in your pilot plan. Budget time and resources for training and adoption. A pilot that users love has a path to production; a pilot that users ignore doesn’t.
5. Create a Path to Production
Many pilots succeed in isolation but have no path to production. They’re built on different infrastructure than production systems. They lack integrations with enterprise tools. They don’t meet security and compliance requirements that production demands. No one owns ongoing maintenance once the pilot team moves on.
Organizations that scale design pilots with production in mind from day one. They use production-like infrastructure from the start so there’s no migration surprise. They build integrations that will scale rather than proof-of-concept workarounds. They document operational requirements—monitoring, alerting, failover, maintenance. They assign ownership for post-pilot operation before the pilot begins.
Before starting a pilot, define what production deployment looks like. Build the pilot to minimize the gap between demo and deployment.
The Scaling Playbook
When you’re ready to scale a successful pilot, the process typically unfolds in four phases.
During the first two weeks, validate value rigorously. Review pilot metrics against the success criteria you defined at the start. Calculate ROI and payback period with real numbers, not projections. Document lessons learned and risks discovered during the pilot. Secure executive sponsorship for scaling—if you can’t get it now, your pilot hasn’t proven enough value.
Weeks three through six are about preparing for production. Address security and compliance gaps identified during the pilot. Build production-grade infrastructure that can handle real load. Create monitoring and alerting that will catch problems before users do. Develop training materials that help users succeed with the new tools.
Weeks seven through ten involve limited rollout. Deploy to a subset of users and monitor closely for issues. Gather feedback and iterate quickly. Validate that production metrics match pilot expectations. This phase catches problems at manageable scale before they become enterprise-wide crises.
From week eleven onward, execute full deployment. Expand to all users with confidence built from the limited rollout. Complete training and communication across the organization. Establish ongoing monitoring that will support the system long-term. Report outcomes to stakeholders to demonstrate value and build support for future initiatives.
Signs You’re Ready to Scale
You’re ready to move from pilot to production when several conditions align. Metrics prove value with clear ROI and documented baselines—not projections, but measured results. Governance is in place with security and compliance sign-off on the production deployment. Infrastructure is ready with production-grade systems that can support scale. Ownership is clear with a team accountable for operation and improvement. Users are engaged, ideally asking for broader access rather than avoiding the pilot. Executive sponsorship is confirmed with leadership committed to the investment required.
Signs You’re Not Ready
Don’t scale if you can’t quantify the business value delivered—enthusiasm isn’t evidence. Don’t scale if security or compliance have outstanding concerns that haven’t been addressed. Don’t scale if users aren’t adopting the pilot solution—production won’t fix adoption problems. Don’t scale if no one owns ongoing operation—orphaned systems become liabilities. And don’t scale if you’re scaling to “prove AI works” rather than solve a business problem—that’s the path to expensive experimentation with no business impact.
The Path Forward
Moving from AI experimentation to business impact requires more than technology. It requires clear business problems with quantified value that justify investment. It requires governance that enables rather than blocks production deployment. It requires metrics that prove outcomes, not just activity. It requires change management that drives adoption. And it requires infrastructure that supports production scale.
The enterprises that master this transition will compound their AI investments, building capability on capability. Those that don’t will keep running pilots—and keep wondering why AI isn’t delivering the transformation they were promised.
The Future of Agentic use case library provides detailed examples of enterprise AI deployments that have successfully made this transition, with architecture patterns and governance frameworks you can adapt.
Ready to scale AI with confidence? Schedule a demo to see how Olakai helps enterprises measure ROI, govern risk, and move from pilot to production.